Remote sensing techniques have been used to generate geographical and spatial information related to an agricultural field. These techniques can be used for monitoring field and/or crop conditions. However, these techniques are of limited use as they only provide two-dimensional images of the field.
Two dimensional field information has also been used for guiding vehicles through a field. In these types of systems, standard cameras, such as CCD devices or video cameras, have been used for detecting the trajectory of an automated vehicle in an agricultural field. However, since no depth information can be efficiently obtained from conventional images, the robustness of such systems rely heavily on the accuracy of the camera calibration, typically performed by means of least mean square methods. As such, conventional machine vision-based vehicle guidance systems have been expensive and unreliable.
One method employed in automatic vehicle guidance systems is to guide farm cultivation equipment through a field based on perceived crop rows. However, conventional crop row detection techniques require significant processing capabilities. For example, the techniques generally require extensive pre-processing algorithms such as binarization processes and threshold calculations in order to accurately identify crop rows from images taken of in an agricultural field scene. In addition, the principal pattern recognition methods used with conventional crop row detection techniques are highly sensitive to noise picked in the field scene images.
Thus, there is a need for an accurate and reliable crop row detection system and method which is easy to operate and inexpensive to maintain.